



"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers.models.xglm.modeling_tf_xglm import (
			    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFXGLMForCausalLM,
			    TFXGLMModel,
			)






@require_tf
class       UpperCamelCase__			:

			"""simple docstring"""


			A__	:					List[str]							  =     XGLMConfig
			A__	:					List[Any]							  =     {}
			A__	:					Optional[Any]							  =     "gelu"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=14						,	SCREAMING_SNAKE_CASE__=7						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=99						,	SCREAMING_SNAKE_CASE__=32						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=4						,	SCREAMING_SNAKE_CASE__=37						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=0.0_2						,	)      ->	int:
				A__											=					parent
				A__											=					batch_size
				A__											=					seq_length
				A__											=					is_training
				A__											=					use_input_mask
				A__											=					use_labels
				A__											=					vocab_size
				A__											=					d_model
				A__											=					num_hidden_layers
				A__											=					num_attention_heads
				A__											=					ffn_dim
				A__											=					activation_function
				A__											=					activation_dropout
				A__											=					attention_dropout
				A__											=					max_position_embeddings
				A__											=					initializer_range
				A__											=					None
				A__											=					0
				A__											=					2
				A__											=					1


			def   snake_case__	(      self		)      ->	List[Any]:
				return XGLMConfig.from_pretrained("facebook/xglm-564M"		)


			def   snake_case__	(      self		)      ->	int:
				A__											=					tf.clip_by_value(
				    ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size		)						,	clip_value_min=0						,	clip_value_max=3		)

				A__											=					None
				if self.use_input_mask:
					A__											=					random_attention_mask([self.batch_size, self.seq_length]		)

				A__											=					self.get_config()

				A__											=					floats_tensor([self.num_hidden_layers, self.num_attention_heads]						,	2		)

				return (
				    config,
				    input_ids,
				    input_mask,
				    head_mask,
				)


			def   snake_case__	(      self		)      ->	str:
				return XGLMConfig(
				    vocab_size=self.vocab_size						,	d_model=self.hidden_size						,	num_layers=self.num_hidden_layers						,	attention_heads=self.num_attention_heads						,	ffn_dim=self.ffn_dim						,	activation_function=self.activation_function						,	activation_dropout=self.activation_dropout						,	attention_dropout=self.attention_dropout						,	max_position_embeddings=self.max_position_embeddings						,	initializer_range=self.initializer_range						,	use_cache=SCREAMING_SNAKE_CASE__						,	bos_token_id=self.bos_token_id						,	eos_token_id=self.eos_token_id						,	pad_token_id=self.pad_token_id						,	return_dict=SCREAMING_SNAKE_CASE__						,	)







			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					self.prepare_config_and_inputs()

				(
				    (
				    A__
				)							,							(
				    A__
				)							,							(
				    A__
				)							,							(
				    A__
				)							,							
				)											=					config_and_inputs

				A__											=					{
				    "input_ids": input_ids,
				    "head_mask": head_mask,
				}

				return config, inputs_dict






@require_tf
class       UpperCamelCase__			(   _lowerCAmelCase       ,		_lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			A__	:					Dict							  =     (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
			A__	:					List[str]							  =     (TFXGLMForCausalLM,) if is_tf_available() else ()
			A__	:					Optional[int]							  =     (
			    {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
			)
			A__	:					Union[str, Any]							  =     False
			A__	:					int							  =     False
			A__	:					Optional[int]							  =     False


			def   snake_case__	(      self		)      ->	Optional[int]:
				A__											=					TFXGLMModelTester(self		)
				A__											=					ConfigTester(self						,	config_class=SCREAMING_SNAKE_CASE__						,	n_embd=37		)


			def   snake_case__	(      self		)      ->	Tuple:
				self.config_tester.run_common_tests()


			@slow
			def   snake_case__	(      self		)      ->	Tuple:
				for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					A__											=					TFXGLMModel.from_pretrained(SCREAMING_SNAKE_CASE__		)
					self.assertIsNotNone(SCREAMING_SNAKE_CASE__		)







			@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."		)
			def   snake_case__	(      self		)      ->	List[Any]:
				super().test_resize_token_embeddings()






@require_tf
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			@slow
			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__=True		)      ->	List[str]:
				A__											=					TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)
				A__											=					tf.convert_to_tensor([[2, 268, 9865]]						,	dtype=tf.intaa		)  # The dog
				# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
				# fmt: off
				A__											=					[2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
				# fmt: on
				A__											=					model.generate(SCREAMING_SNAKE_CASE__						,	do_sample=SCREAMING_SNAKE_CASE__						,	num_beams=1		)
				if verify_outputs:
					self.assertListEqual(output_ids[0].numpy().tolist()						,	SCREAMING_SNAKE_CASE__		)


			@slow
			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					XGLMTokenizer.from_pretrained("facebook/xglm-564M"		)
				A__											=					TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)

				tf.random.set_seed(0		)
				A__											=					tokenizer("Today is a nice day and"						,	return_tensors="tf"		)
				A__											=					tokenized.input_ids
				# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
				with tf.device(":/CPU:0"		):
					A__											=					model.generate(SCREAMING_SNAKE_CASE__						,	do_sample=SCREAMING_SNAKE_CASE__						,	seed=[7, 0]		)
				A__											=					tokenizer.decode(output_ids[0]						,	skip_special_tokens=SCREAMING_SNAKE_CASE__		)

				A__											=					(
				    "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
				)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)







			@slow
			def   snake_case__	(      self		)      ->	int:
				A__											=					TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)
				A__											=					XGLMTokenizer.from_pretrained("facebook/xglm-564M"		)

				A__											=					"left"

				# use different length sentences to test batching
				A__											=					[
				    "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
				    "left-padding, such as in batched generation. The output for the sequence below should be the same "
				    "regardless of whether left padding is applied or not. When",
				    "Hello, my dog is a little",
				]

				A__											=					tokenizer(SCREAMING_SNAKE_CASE__						,	return_tensors="tf"						,	padding=SCREAMING_SNAKE_CASE__		)
				A__											=					inputs["input_ids"]

				A__											=					model.generate(input_ids=SCREAMING_SNAKE_CASE__						,	attention_mask=inputs["attention_mask"]						,	max_new_tokens=12		)

				A__											=					tokenizer(sentences[0]						,	return_tensors="tf"		).input_ids
				A__											=					model.generate(input_ids=SCREAMING_SNAKE_CASE__						,	max_new_tokens=12		)

				A__											=					tokenizer(sentences[1]						,	return_tensors="tf"		).input_ids
				A__											=					model.generate(input_ids=SCREAMING_SNAKE_CASE__						,	max_new_tokens=12		)

				A__											=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE__						,	skip_special_tokens=SCREAMING_SNAKE_CASE__		)
				A__											=					tokenizer.decode(output_non_padded[0]						,	skip_special_tokens=SCREAMING_SNAKE_CASE__		)
				A__											=					tokenizer.decode(output_padded[0]						,	skip_special_tokens=SCREAMING_SNAKE_CASE__		)

				A__											=					[
				    "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
				    "left-padding, such as in batched generation. The output for the sequence below should be the same "
				    "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
				    "a single",
				    "Hello, my dog is a little bit of a shy one, but he is very friendly",
				]
				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	[non_padded_sentence, padded_sentence]		)












"""simple docstring"""





import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_SCREAMING_SNAKE_CASE   :							int          =		pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
_SCREAMING_SNAKE_CASE   :							List[Any]          =		dataset.iloc[:, 1:2].values
_SCREAMING_SNAKE_CASE   :							str          =		dataset.iloc[:, 2].values


_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Dict          =		train_test_split(X, y, test_size=0.2, random_state=0)


_SCREAMING_SNAKE_CASE   :							Optional[int]          =		PolynomialFeatures(degree=4)
_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		poly_reg.fit_transform(X)
_SCREAMING_SNAKE_CASE   :							List[str]          =		LinearRegression()
pol_reg.fit(X_poly, y)







def 	lowerCamelCase__			(				) ->							List[str]:
  plt.scatter(_lowerCamelCase						,							_lowerCamelCase						,							color='red'					)
  plt.plot(_lowerCamelCase						,							pol_reg.predict(poly_reg.fit_transform(_lowerCamelCase					)					)						,							color='blue'					)
  plt.title('Truth or Bluff (Linear Regression)'					)
  plt.xlabel('Position level'					)
  plt.ylabel('Salary'					)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003




